Medical images represent various parts of the human body like blood vessels, bones, etc. By analyzing the medical images clinicians can plan treatment for the patients and at the same time, the clinicians can be guided to operate the body part while operating over the body part, for example catheter guidance, image guidance for the surgery. The medical images for surgery guidance are analyzed by registering the medical image with pre-operative 3D medical data. During the registration, the blood vessels represented by the medical images are used as features, which further requires segmentation of the blood vessels in real-time. Currently, segmentation of the blood vessels is done manually by clinicians which involves manual input of multiple points along the blood vessel direction to get blood vessel parameters like diameter and stenoses detection. Such manual segmentations have reduced efficiency of clinicians, mis-detection of blood vessels, etc. Manual segmentation is slow and prone to human errors, thus automatic segmentation of the blood vessels is desired.
One probable way is disclosed by Matthias Schneider and Hari Sundar in “Automatic global vessel segmentation and catheter removal using local geometry information and vector field integration”, where local probability map is combined with local directional vessel information to result into global vessel segmentation, where the segmentation is represented as a set of discrete streamlines populating the vascular structures and providing additional connectivity and geometric shape information and the streamlines are computed by numerical integration of the directional vector field that is obtained from eigen analysis of the local Hessian indicating the local vessel direction.